The fluctuant, nonlinear and chaotic characteristics of the stock market have long been a topic of interest for investors and financial researchers. This study attempts to exploit computer technology, financial mathematics, and econometrics to make reasonable investment decisions to reduce man-made errors or mistakes and to increase profits. This work implements a system which is an application of eXtended Classifier System (XCS), which incorporates features such as dynamic learning and group decision making. An empirical study is conducted by comparing the profitability of the proposed system with that of investment strategies based on simple rules with single technical indices, individual learning XCS, buy and hold, and six-year term deposit. The proposed system demonstrates superior performance in terms of accuracy, rate of cumulative return, and variance of return.